Subject statistics
First, we will determine whether IQ correlates with age in our sample. As specified in our pre-registration, here we run run a linear regression testing the effects of age on IQ.
Analysis of Variance Table
| Age |
1 |
1375 |
1375 |
8.642 |
0.004194 |
0.08943 |
| Residuals |
88 |
14005 |
159.1 |
NA |
NA |
NA |
This analysis indicates that there is a significant relation between age and IQ. As such, we will run all subsequent models both with and without IQ as an interacting fixed effect to better tease apart effects of age from effects of IQ.
Frequency task analysis
In this section, we will run mixed effects models to determine the effects of appearance count and age on participants’ accuracy and reaction time during the frequency task. For both models, we will start with the maximal random effects structure. If the models do not converge, we will remove random slopes and intercepts as specified in the pre-registration. As we preregistered, if the maximal model does not converge, we will follow the procedure laid out in Singmann & Kellen (2017) to reach convergence. They recommend first removing correlations between random slopes and random intercepts. If models stil fail to converge, they recommend removing random-effects parameters ’starting with the highest-order random effects parameter with the lowest estimated variance.
Frequency task accuracy
First, we will ensure that all subjects responded to >10% of repeated trials, indicating that they understood the task instructions.
Frequency accuracy mixed-effects model: New items without IQ
## Fitting 2 (g)lmer() models:
## [..]
## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: acc ~ ageScaled + (1 | ID) + (ageScaled | animal)
## Data: freqDataNewItems
## Df full model: 6
## Effect df Chisq p.value
## 1 ageScaled 1 19.92 *** <.0001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Frequency accuracy mixed-effects model: New items with IQ
## Fitting 4 (g)lmer() models:
## [....]
## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: acc ~ ageScaled * IQScaled + (1 | ID) + (ageScaled * IQScaled |
## Model: animal)
## Data: freqDataNewItems
## Df full model: 15
## Effect df Chisq p.value
## 1 ageScaled 1 18.24 *** <.0001
## 2 IQScaled 1 0.61 .43
## 3 ageScaled:IQScaled 1 5.14 * .02
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Frequency accuracy: Repeated items without IQ
## Fitting 4 (g)lmer() models:
## [....]
## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: acc ~ ageScaled * appearanceCountScaled + (appearanceCountScaled |
## Model: ID) + (ageScaled * appearanceCountScaled | animal)
## Data: freqDataRepeatedItems
## Df full model: 17
## Effect df Chisq p.value
## 1 ageScaled 1 6.71 ** .010
## 2 appearanceCountScaled 1 54.93 *** <.0001
## 3 ageScaled:appearanceCountScaled 1 3.54 + .06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Frequency accuracy: Repeated items with IQ
## Fitting 8 (g)lmer() models:
## [........]
## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: acc ~ ageScaled * IQScaled * appearanceCountScaled + (appearanceCountScaled |
## Model: ID) + (ageScaled * IQScaled * appearanceCountScaled | animal)
## Data: freqDataRepeatedItems
## Df full model: 47
## Effect df Chisq p.value
## 1 ageScaled 1 7.30 ** .007
## 2 IQScaled 1 10.24 ** .001
## 3 appearanceCountScaled 1 48.99 *** <.0001
## 4 ageScaled:IQScaled 1 0.09 .77
## 5 ageScaled:appearanceCountScaled 1 1.14 .29
## 6 IQScaled:appearanceCountScaled 1 0.91 .34
## 7 ageScaled:IQScaled:appearanceCountScaled 1 1.43 .23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Frequency accuracy: Plot
Frequency task reaction times: Repeated items without IQ
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]
## Mixed Model Anova Table (Type 3 tests, KR-method)
##
## Model: freqRT ~ appearanceCountScaled * ageScaled + (appearanceCountScaled |
## Model: ID) + (appearanceCountScaled * ageScaled | animal)
## Data: freqRTdata
## Effect df F p.value
## 1 appearanceCountScaled 1, 70.06 103.09 *** <.0001
## 2 ageScaled 1, 91.59 10.63 ** .002
## 3 appearanceCountScaled:ageScaled 1, 70.90 6.49 * .01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Frequency task reaction times: Repeated items with IQ
## Fitting one lmer() model. [DONE]
## Calculating p-values. [DONE]
## Mixed Model Anova Table (Type 3 tests, KR-method)
##
## Model: freqRT ~ appearanceCountScaled * ageScaled * IQScaled + (appearanceCountScaled |
## Model: ID) + (appearanceCountScaled * ageScaled * IQScaled | animal)
## Data: freqRTdata
## Effect df F p.value
## 1 appearanceCountScaled 1, 69.72 90.58 *** <.0001
## 2 ageScaled 1, 89.98 14.25 *** .0003
## 3 IQScaled 1, 89.71 4.44 * .04
## 4 appearanceCountScaled:ageScaled 1, 70.32 7.62 ** .007
## 5 appearanceCountScaled:IQScaled 1, 70.10 0.77 .38
## 6 ageScaled:IQScaled 1, 93.57 0.28 .60
## 7 appearanceCountScaled:ageScaled:IQScaled 1, 69.85 0.65 .42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Frequency task reaction times: Plot
Memory test analysis
In this section, we will run four mixed effects models to examine the effects of frequency condition on memory accuracy (both with and without IQ included). We will deviate from our pre-registration by only analyzing the data from the first appearance of each item during the memory test (the first 30 trials). We will follow the same procedure as with our frequency data to find the maximal random effects structure that converges.
Memory accuracy without IQ
## Fitting 4 (g)lmer() models:
## [....]
## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: memAcc ~ freqCondScaled * ageScaled + (freqCondScaled || ID) +
## Model: (freqCondScaled * ageScaled || animal)
## Data: memDataCombined
## Df full model: 10
## Effect df Chisq p.value
## 1 freqCondScaled 1 0.51 .48
## 2 ageScaled 1 9.15 ** .002
## 3 freqCondScaled:ageScaled 1 0.02 .89
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Memory accuracy with IQ
## Fitting 8 (g)lmer() models:
## [........]
## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: memAcc ~ freqCondScaled * ageScaled * IQScaled + (freqCondScaled ||
## Model: ID) + (freqCondScaled * ageScaled * IQScaled || animal)
## Data: memDataCombined
## Df full model: 18
## Effect df Chisq p.value
## 1 freqCondScaled 1 0.01 .93
## 2 ageScaled 1 14.69 *** .0001
## 3 IQScaled 1 11.18 *** .0008
## 4 freqCondScaled:ageScaled 1 0.17 .68
## 5 freqCondScaled:IQScaled 1 0.59 .44
## 6 ageScaled:IQScaled 1 0.27 .60
## 7 freqCondScaled:ageScaled:IQScaled 1 3.50 + .06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Relationship between structural learning and memory encoding
As specified, we will now run a regression examining the effects of structural learning index on memory benefit score, to determine if participants who showed the most evidence of learning the animal frequencies also demonstrated the greatest effect of frequency condition on encoding.
Structural learning model without IQ
| (Intercept) |
-0.09911 |
0.1035 |
-0.9578 |
0.3409 |
| ageScaled |
0.03167 |
0.1044 |
0.3034 |
0.7623 |
| learningIndexScaled |
0.007472 |
0.1064 |
0.07022 |
0.9442 |
| ageScaled:learningIndexScaled |
0.01561 |
0.09922 |
0.1573 |
0.8753 |
Fitting linear model: memBenefitIndex ~ ageScaled * learningIndexScaled
| 89 |
0.9726 |
0.001356 |
-0.03389 |
Structural learning model with IQ
Table continues below
| (Intercept) |
-0.03462 |
0.1069 |
-0.3238 |
| ageScaled |
0.08747 |
0.1116 |
0.7838 |
| IQScaled |
0.0485 |
0.1104 |
0.4392 |
| learningIndexScaled |
0.012 |
0.1099 |
0.1092 |
| ageScaled:IQScaled |
0.248 |
0.1073 |
2.311 |
| ageScaled:learningIndexScaled |
-0.007685 |
0.1027 |
-0.07484 |
| IQScaled:learningIndexScaled |
0.06565 |
0.1144 |
0.5736 |
| ageScaled:IQScaled:learningIndexScaled |
0.05675 |
0.1117 |
0.5081 |
| (Intercept) |
0.7469 |
| ageScaled |
0.4355 |
| IQScaled |
0.6617 |
| learningIndexScaled |
0.9133 |
| ageScaled:IQScaled |
0.02336 |
| ageScaled:learningIndexScaled |
0.9405 |
| IQScaled:learningIndexScaled |
0.5678 |
| ageScaled:IQScaled:learningIndexScaled |
0.6127 |
Fitting linear model: memBenefitIndex ~ ageScaled * IQScaled * learningIndexScaled
| 89 |
0.9611 |
0.07074 |
-0.009569 |
Plot: Correlation between frequency learning and memory
Plot: Interaction between age and IQ on memory benefit score
Relationship between strategy use and memory encoding
Finally, we will run a regression examining the effects of strategy use on memory benefit score. We will model the effect of subject strategy recognition (a 2-level categorical variable based on participant’s response (yes/no) to our question of whether they recognized that focusing on the more-frequent animals would be a good strategy) and age on their memory benefit score.
Strategy use regression without IQ
| (Intercept) |
-0.6771 |
0.2296 |
-2.949 |
0.004117 |
| ageScaled |
0.03278 |
0.2102 |
0.156 |
0.8764 |
| GeneratedStrategy1 |
0.7153 |
0.2545 |
2.811 |
0.006128 |
| ageScaled:GeneratedStrategy1 |
-0.04419 |
0.2385 |
-0.1853 |
0.8535 |
Fitting linear model: memBenefitIndex ~ ageScaled * GeneratedStrategy
| 89 |
0.9288 |
0.08929 |
0.05715 |
Strategy use regression with IQ
Table continues below
| (Intercept) |
-0.4431 |
0.2677 |
-1.655 |
| ageScaled |
0.2314 |
0.2609 |
0.8868 |
| IQScaled |
-0.002351 |
0.2269 |
-0.01036 |
| GeneratedStrategy1 |
0.5094 |
0.2902 |
1.755 |
| ageScaled:IQScaled |
0.4505 |
0.2165 |
2.081 |
| ageScaled:GeneratedStrategy1 |
-0.2019 |
0.286 |
-0.7058 |
| IQScaled:GeneratedStrategy1 |
0.07963 |
0.256 |
0.311 |
| ageScaled:IQScaled:GeneratedStrategy1 |
-0.3265 |
0.2473 |
-1.32 |
| (Intercept) |
0.1017 |
| ageScaled |
0.3778 |
| IQScaled |
0.9918 |
| GeneratedStrategy1 |
0.083 |
| ageScaled:IQScaled |
0.04058 |
| ageScaled:GeneratedStrategy1 |
0.4823 |
| IQScaled:GeneratedStrategy1 |
0.7566 |
| ageScaled:IQScaled:GeneratedStrategy1 |
0.1904 |
Fitting linear model: memBenefitIndex ~ ageScaled * IQScaled * GeneratedStrategy
| 89 |
0.9123 |
0.1627 |
0.09031 |
Strategy use by age group
| Child |
0.7667 |
| Adolescent |
0.8333 |
| Adult |
0.8333 |